Research and development drives success in shale plays throughout the world, enabling operators to deploy new drilling, completions, and production technologies to reach more reservoir area and extend the life of production wells. This work demonstrates the development, validation, and deployment of an extreme torque casing connection addressing technical challenges of tubulars in unconventionals.
Throughout the well lifetime, Oil Country Tubular Goods (OCTG) experience various loads during the installation, stimulation, and production phases. Some of the challenges experienced during the stimulation and production phases relate to internal and external pressure resistance, sealability, corrosion and cracking, erosion, and wear. Furthermore, with the increase in lateral length and the more demanding well geometries, the OCTG capabilities related to high cycle fatigue, connection runability, and torque limits become more important to safely and efficiently reach the total depth of the well and ensure integrity throughout well life. Another scenario in which the torque limit of an OCTG connection is important is rotating while cementing, a practice undertaken to mitigate sustained casing pressure, improve well integrity, and completion efficiency.
We present the key elements in the development of a casing connection that overcomes these challenges and the decision process leading to a prototype. To prove the design concept, a fit-for-purpose testing protocol was adopted to validate its performance, replicating the installation, stimulation, and production phases under the expected loads. Once validated, a pilot involving casing installation, rotation while cementing and stimulation was completed in two wells, and its outcomes will be discussed in this work.
This novel casing extreme torque connection, designed to overcome the application challenges, enables the installation of casing in longer laterals, together with the improvement of well integrity through rotation while cementing.
The performance of the product, tested through a special procedure while ensuring reliability, was confirmed by the case study from the Niobrara shale. A new connection considering the challenges of wells in unconventional plays must account for several aspects from design to installation. We show the process, from the design stage and validation, leading to successful field deployment.
Hydraulic fracturing is a typical and vital technique applied in shale gas reservoir development. Numerical simulation used to be a common tool to optimize the parameters in hydraulic fracturing design determining the stage numbers, injection pressure, proppant amount, etc. However, the current understanding of shale gas storage and transport mechanism (e.g. adsorption/desorption, diffusion) is basically adopted from the lessons learned from coal seams through past experience, which might not help an efficient numerical simulation development.
In this study, how artificial intelligence assisted data driven models assist the hydraulic fracturing design in shale gas reservoir is discussed. It starts by collecting field data and generate a spatial-temporal database including reservoir characteristics, operational/production information, completion/stimulation data and other variables, Neural Network models are then developed to study the impacts of all parameters on gas production as well as perform history matching of the field history. The AI assisted model with acceptable matching of field data can be used to model different hydraulic fracturing design scenarios and provide predictions on well production.
Use of diverters for altering fluid distribution among created hydraulic fractures in horizontal wells has gained popularity in recent years, both for initial and re-fracturing treatments. Aims in initial fracturing treatments have included creating more uniform distribution of slurry within the created fractures, increasing stage efficiency by reducing the number of pumping stages while increasing the number of clusters per stage, increasing the number of fractures created in openhole completions, reducing interactions between fractures in adjacent horizontal wells, etc. In re-fracturing treatments, a popular application is for altering fluid distribution in wells re-treated without isolation between stages (Pump & Pray/Bullheading) with the intent of increasing the number of re-activated fractures and initiating new fractures through added perforations.
Engineering analysis of the mechanics of fluid diversion has not received the same degree of attention as its use. The reported discussions are often limited in their scope, two-dimensional in structure, and somewhat speculative in their conclusions.
This paper divides the targets of diversion into three categories; at the wellbore/perfs, near wellbore, and deeper inside the fracture. It divides the types of diverters into three categories, mechanical, solid particulate (including proppants), and chemical. The applications are divided into two categories, initial and re-fracturing, together with highlighting their differences and requirements for successful diversion. The paper discusses how presence of proppant changes the fluid distribution in favor of more conductive perforations. It considers the fracture as a three-dimensional structure, extending on both sides of the wellbore. It describes how different diverting agents cause fluid redistribution between the fractures, and the important role of proppant in some applications. It shows that as the target of fluid diversion moves away from the wellbore the chances of its success become smaller and more unpredictable, while also the time before effective diversion takes place becomes longer.
Comprehensive understanding of the mechanics of fluid diversion helps in the selection of the type of diverter and how best to deploy it for achieving specific objectives and results.
The paper provides a technical overview of an operator's Real-Time Drilling (RTD) ecosystem currently developed and deployed to all US Onshore and Deepwater Gulf of Mexico rigs. It also shares best practices with the industry through the journey of building the RTD solution: first designing and building the initial analytics system, then addressing significant challenges the system faces (these challenges should be common in drilling industry, especially for operators), next enhancing the system from lessons learned, and lastly, finalizing a fully integrated and functional ecosystem to provide a one-stop solution to end users.
The RTD ecosystem consists of four subsystems as shown in architecture
RTD ecosystem architecture
RTD ecosystem architecture
All of these subsystems are fully integrated and interact with each other to function as one system, providing a one-stop solution for real-time drilling optimization and monitoring. This RTD ecosystem has become a powerful decision support tool for the drilling operations team. While it was a significant effort, the long term operational and engineering benefits to operators designing such a real-time drilling analytics ecosystem far outweighs the cost and provides a solid foundation to continue pushing the historical limitations of drilling workflow and operational efficiency during this period of rapid digital transformation in the industry.
Potapenko, Dmitriy (Schlumberger) | Theuveny, Bertrand (Schlumberger) | Williams, Ryan (Schlumberger) | Moncada, Katharine (Schlumberger) | Campos, Mario (Schlumberger) | Spesivtsev, Pavel (Schlumberger) | Willberg, Dean (Schlumberger)
Highly efficient multi-stage hydraulic fractured horizontal wellbores are the dominant completion method for many basins worldwide. One potential weakness of multi-stage hydraulic fracturing is that the later stages of the completion workflow – frac-plug drill out (FPDO) and flowback – cause large pressure fluctuations and transient flows through the perforation clusters that coincide with a period of low closure stress in the fractures. The proppant packs in the fractures during this period are fragile and prone to failure. Previously reported results show that flowback and initial production practices have a major impact on proppant production, maintenance and disposal costs and the subsequent well performance. In this paper the results from over 200 FPDO and flowback operations from the United States and Argentina are reviewed. These results show that maintaining a balanced flowrate during FPDO operations is critical for minimizing inadvertent damage to the hydraulic fracture network.
The FPDO flowrate balance is the difference between the coiled tubing injection and annular return flowrates. The magnitude and sign of the balance corresponds to the instantaneous flowrate through the open perforation clusters into or out of the hydraulic fracture network. A positive balance rate, or overbalance, injects fluid into the fracture system. A negative balance rate, or underbalance, produces stimulation or formation fluids from the fracture network. Sudden changes between these two regimes creates local flows that can be severe enough to flush large quantities of proppant out of the fractures. Our results show that high-frequency multiphase flowmeters simplify the process of maintaining balance (no inflow, no outflow). Furthermore, close monitoring of any imbalance that develops, and rapid control of the surface choke and injection rate, can provide for an efficient operation while protecting the integrity of the fracture system.
Early monitoring of flowback and production with a high frequency flowmeter was shown to be extremely useful technique for optimizing well productivity during well clean-up. This paper also shows how a dual energy gamma ray multiphase flowmeter successfully quantified proppant produced during FPDO and flowback. Examples of the dynamics of sand production are shown, as well as correlations to events of excessive underbalance conditions.
At the end of the paper we show that most of the highlighted problems can be solved through making changes to the well construction workflow and accounting for relationships between various well operations. Incorporation of this workflow enables early prediction of well performance issues and their efficient resolution.
We present an assessment of the impact of low-salinity brine osmosis on oil recovery in liquid-rich shale reservoirs. The paper includes: (1) membrane behavior of shales when contacted by low-salinity brine, (2) numerical model of osmosis mass transport for low-salinity brine, and (3) enhanced oil recovery (EOR) potential of low-salinity osmosis in liquid-rich shale reservoirs. Capillary osmosis causes low-salinity brine to be imbibed into the shale matrix; thus, forcing expulsion of oil from the rock matrix. This oil recovery process is described by a multi-component mass transport mathematical model consisting of advective and molecular transport of water molecules and dissolved ions. In the transport model, the activity-corrected diffusion of the brine solution is used to calculate the volume of brine imbibed into a shale core sample and the resulting expelled oil. We used the mathematical model to match oil recovery from two carefully designed brine-imbibition experiments conducted at Colorado School of Mines. We have concluded that, in oil-wet shale reservoirs, low-salinity brine invasion of the rock matrix is by osmosis rather than capillary force. Thus, osmosis is the only imbibing force that drives the low salinity brine into the reservoir rock matrix. Furthermore, we believe brine osmosis can potentially enhance oil recovery by expelling oil out of the rock matrix and into the micro-and macro-fractures existing in the stimulated reservoir volume.
Hydrocarbon production from shale formation has become an essential part of the global energy supply in the past decade. The life of a project in an unconventional play significantly depends on the prediction of Estimated Ultimate Recovery (EUR). However, the conventional methodology to predict EUR becomes less accurate for shale formations, which significantly affects the economics returns of projects in unconventional plays. The objective of this article is to investigate the most important independent variables, including petrophysics and completion parameters, to estimate EUR by the machine learning algorithm. A novel machine learning model based on Random Forest Regression is introduced to predict EUR and to rank the importance of the independent variables.
In this article, production/petrophysics/engineering/ data with more than 25 variables from 4000 wells in Eagle Ford is summarized for analysis. The data is collected from production monitoring, well logging, well testing, seismic interpretation and lab experiments. This paper has three major components. Firstly, a multivariate linear regression model is created to predict the overall EUR. Secondly, the spatial autocorrelation analysis is carried out to identify whether spatial variables could affect the accuracy of the multivariate regression model. Thirdly, the Random Forest Regression models are trained to examine their reliability in predicting EUR with spatially autocorrelated data. The importance of key predictors is also identified. The final models are tuned with optimized hyperparameters. Through the article, the predictive capabilities of each Random Forest Regression model are discussed in detail to understand the physics behind unconventional hydrocarbon production mechanisms.
The results and workflow presented in this paper are insightful and novel. Firstly, we test the multivariate regression analysis with all the petrophysics and completion variables using the backward elimination method. This widely used model has a limitation of excluding the spatial information. In order to identify the impact of spatial variable, we calculate the Moran's Index and find out that the data in this study is clustered or spatially autocorrelated. The p-value for EUR, Oil EUR and Gas EUR are 0.000002, 0.000000 and 0.12, which all reject the null hypothesis that the data is randomly distributed. To include the spatial information in the prediction, we use advanced machine learning technology, Random Forest, to predict the EUR with a combination of petrophysics, completion variables and spatial information. The key variables to predict EUR, Oil EUR and Gas EUR by the Random Forest Regression are identified. However, the importance of the key variables to predict Oil EUR and Gas EUR are different. Therefore, we split the overall EUR Random Forest Regression model (57% explained) into two prediction models, one for Oil EUR prediction and one for Gas EUR prediction. The Gas EUR Random Forest Regression model has better performance (76% explained) compared to the Oil EUR Random Forest Regression model (60% explained).
This study provides a deeper understanding of unconventional hydrocarbon production prediction from a big data perspective, and proposes a novel and reliable machine-learning model to predict EUR to evaluate economic returns in Eagle Ford. Compared to the traditional multivariate regression model, our Random Forest Regression models are more reliable. In addition, the Random Forest technique is able to rank the importance of the relevant independent variables, and the rank of importance can be applied to guide and to improve data collection and model training for further study on this topic. The workflow presented in this article can be also used to train data for other unconventional resource plays.
The Denver Section Emerging Leaders Program (ELP) has been up and running for 1 year now. Thus far, we have held several networking socials and luncheon meetings with lecturers from around the Rocky Mountain region. Topics have included professional licensing for petroleum engineers and challenges facing the natural gas market in the Rocky Mountain region. Industry financial and other resource support has been overwhelming. Thanks to all who have contributed.
This seminar will teach participants how to identify, evaluate, and quantify risk and uncertainty in everyday oil and gas economic situations. It reviews the development of pragmatic tools, methods, and understandings for professionals that are applicable to companies of all sizes. The seminar also briefly reviews statistics, the relationship between risk and return, and hedging and future markets. Strategic thinking and planning are key elements in an organisation’s journey to maximise value to shareholders, customers, and employees. Through this workshop, attendees will go through the different processes involved in strategic planning including the elements of organisational SWOT, business scenario and options development, elaboration of strategic options and communication to stakeholders.
Digital technologies serve as a primary theme of this year’s group, with a few environmentally conscious firms included in the mix. The well will immediately be brought on production and is expected to flow at more than 100 MMscf/D of gas and 3,000 B/D of associated condensate, the company said. The main goal of production logging is to evaluate the well or reservoir performance. The shale sector is studying the results of a 23-well experiment in the southeastern corner of New Mexico to learn what the wider implications might be. The shale sector is making moves to consolidate amid investor pressure to increase cash flow.